Abstract:
Rawalpindi city is growing at a very fast rate, due to the migration of the people from rural areas towards the urban areas. The increase in built-up area resultant from increased Land Surface Temperature (LST), due to the dense population and urban sprawl, which has inflamed the quality of life as well as the energy demand. The rise in temperature in Rawalpindi resulted in Urban Heat Islands (UHI) and the surface temperature was high when compared to nearby suburban areas. Thus, this study focused on mapping and analysis of the Land use Land cover (LULC) and LST by time-series data for the period from 1990 to 2020 through Landsat data and Google Earth Engine. The research aims at the spatial-temporal analysis of LULC change and LST dynamics and their infrastructure on socio-environmental and energy demands carried out through Landsat data. The results revealed that the urban growth of the city has increased from 7.5 to 29.7 %, resulting in an increase in LST by 3.04°C from 1990 to 2020, with an annual increase of 0.12°C. The statistical analysis between LULC and LST revealed the transition of the major portion of the vegetation and barren lands into the built-up area causing of high surface temperature that identifies the UHI’s in the city. These UHI’s affected areas in 2020 were large approx. 8% when compared to previous years from 1990 to 2010. The correlation of the two environmental variables built-up and vegetation with LST shows, that there is a strong relationship of built-up with LST than vegetation. Future predictions of LULC depict that, there is a linear increasing trend of built-up with a 13.8% increase from 2020 to 2030, which would increase the temperature, and would cause an increase in energy demands, and disorder the quality of life. The result concluded that there is a need to take immediate actions to measure the mitigation of devastating effects of LST by sustainable management, encouragement of plantation, smart approaches towards vertical buildings, and the conversion of barren land to vegetation.
